Covariate Effect Visualizer

Analyzer

Visualize covariate effects on PK parameters as a forest plot. Assess clinical significance with equivalence bounds. Free, instant, with AI interpretation.

PK Parameter Name

Effect Type

Reference Patient Sliders

Lower Bound

Upper Bound

Reference Lines

BE Bounds (0.80-1.25): 0.8Reference (1.0): 1

Covariate Effects (ref = 1.0)

Covariate

Est

Covariate

Est

Covariate

Est

Covariate

Est

Covariate

Est

Covariate

Est

Results

Summary

1 significant, 2 potentially significant, 3 not significant out of 6 covariates

Clinically Significant

Potentially Significant

Not Significant

Parameter

Significant

1.000

Potentially Significant

2.000

Not Significant

3.000

Forest Plot — CL

Weight (>90 kg vs 70 kg)

1.15 [1.02–1.3]

Age (>65 yr vs 40 yr)

0.92 [0.82–1.03]

Renal Impairment (Mild)

1.28 [1.08–1.52]

Hepatic Impairment (Mild)

1.05 [0.88–1.25]

Female vs Male

0.95 [0.86–1.05]

Asian vs Caucasian

1.08 [0.92–1.27]

Forest Plot

Interpretation

Caution

1 covariate(s) show clinically significant effects on CL. Dose adjustment may be warranted for affected subpopulations. Per FDA 2022 popPK guidance, include forest plots of covariate effects in the NDA clinical pharmacology section.

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For educational and research purposes only. Not intended as medical advice. Always verify calculations independently and consult qualified healthcare professionals. See our Terms of Service for full details.

Educational Content